Topic: foundation models

Investigate foundation models for safe, socially aware human–robot interaction in the HUCEBOT team. The ENACT-funded project combines vision-language and vision-language-action models with motion planning and control, addressing hallucinations, long-horizon behavior, human communication and environmental adaptability. Socially aware robot navigation is the application domain. Applicants need a doctorate, Python/PyTorch, robotics knowledge and English; foundation-model experience is desirable. Appointment: 12 months; advertised start 2027-01-01. €2788 gross/month.

VIB.AI and KU Leuven seek a group leader and professor developing AI and mechanistic models for biological research. Possible areas include genome-regulation foundation models, protein structure and interactions, cellular imaging, disease models, digital twins and experimental feedback. The role combines independent research, supervision, teaching and institutional service. Salary and core research funding are included; the latter is renewable in five-year periods. The planned start is in 2027.

The Haibe-Kains Lab at Princess Margaret Cancer Centre seeks a researcher for computational oncology, biomarker discovery and treatment-response modelling. Work combines genomic, molecular and clinical data using multimodal learning and foundation models, with reproducible, privacy-aware and federated workflows across collaborating institutions. The temporary full-time appointment lasts two years, with 37.5 hours per week and annual compensation of CAD 54,902–82,352.

Foundation Models for the Brain and Body is the second edition of a NeurIPS workshop on large-scale pretrained AI models of neural and physiological signals. Advances in brain interfacing and wearable technologies, including EEG, intracortical electrophysiology, EMG, MEG and ECG, now yield large and diverse biosignal datasets collected across real-world contexts, but these timeseries are noisy, heterogeneous and recorded under variable conditions across subjects, devices and environments. The workshop brings together neuroscientists, biomedical engineers, wearable-technology researchers and ma

EMBO | EMBL Symposium bringing machine-learning and life-science researchers together across sequencing, imaging and structural biology. Topics include foundation models, cross-modality learning, causal explanation, mechanistic interpretability, generative models and AI agents. Onsite workshops and tutorials take place on the first day; the following three days offer hybrid talks and posters. Registration is not yet open; the official page accepts expressions of interest.

Foundation models and synthetic data are increasingly used in scientific applications, yet the reliability of their outputs is often uncertain. This workshop investigates how model predictions and generated data can be incorporated into statistical inference without losing trustworthy uncertainty estimates. Researchers from several disciplines will study the implications of black-box models for inference and decision-making and develop methods for assessing and improving reliability.

Ai2's Robotics team is hiring a research scientist to investigate foundation models for robotics and develop open methods, datasets, benchmarks, and models. The work combines large-scale simulation with real robots, spanning manipulation, planning and control, reinforcement or imitation learning, sim-to-real transfer, world models, and broadly competent robotic systems.

Lead computational work on protein structure, design and antibody discovery. Develop methods that connect protein foundation models, antigen and binder datasets, biophysical prediction and experimental results, while building reliable data workflows and GPU-enabled tools. The position includes team leadership and collaboration with laboratory scientists. This hybrid role advertises USD 200,000–250,000. Applications are currently available without a fixed closing date.

University-wide faculty recruitment covers core AI methods and interdisciplinary applications. Topics include learning theory, language and foundation models, robotics, trustworthy AI and AI systems. Appointees join a suitable home department, develop externally funded research, teach and mentor students, and collaborate across disciplines. Initial review begins on 1 December 2026.

Develop AI-based software tools for materials research and scientific computing. The work explores how foundation models and generated code can improve research workflows and computational methods.

Oden Institute Seminar by Rex Ying (Yale University) on how non-Euclidean geometries, particularly hyperbolic geometry, can enhance foundation models by better capturing hierarchies and symmetries in real-world data, with applications across Transformers, language model training, multimodal systems, and recommender systems.

Archive listing for the EMBO | EMBL Symposium held on 10–13 March 2026 at EMBL Heidelberg and online. The meeting connected researchers applying artificial intelligence to sequencing, imaging and structural biology. Programme themes included foundation models, learning across data types, interpretability, generative modelling and translating algorithms into biological insight. The official event page retains the programme and speaker information. This event has ended; registration and abstract submission are closed.

For decades dynamical systems theory played a pivotal role in theoretical and computational neuroscience, as it links biophysical and biochemical processes to neural computation. In fact, dynamical systems are computationally universal. Rather than hand-crafting computational theories of neural function based on dynamical systems, recent developments in scientific machine learning (ML) and AI suggest that we may be able to infer such dynamical-computational models directly from neurophysiological and behavioral observations. This is called dynamical systems reconstruction (DSR), the learning o

Andreas Tolias discusses foundation models trained on large neuroscience datasets and their potential to generalize across experimental settings.

Surya Ganguli and Bowen Liu join Vijay Pande to examine how machine learning can support biological discovery and drug development. They compare physics-based modelling, learned molecular representations and self-supervised foundation models; discuss protein structures, target identification and generative molecule design; and explain why experimental validation and generalisation beyond training data remain difficult. The conversation also considers interpretable cellular representations and the potential role of AI in clinical-trial recruitment and prediction. The publisher provides an accom

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-tra